Intangible Dynamics: Knowledge Assets in the Context of Big Data and Business Intelligence

2018 ◽  
pp. 325-354 ◽  
Author(s):  
G. Scott Erickson ◽  
Helen N. Rothberg
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Knowledge Assets

Applying a Business Intelligence System in a Big Data Context: Production Companies

2020 ◽  
pp. 268-274
Author(s):  
Jesús Silva ◽  
Mercedes Gaitán ◽  
Noel Varela ◽  
Doyreg Maldonado Pérez ◽  
Omar Bonerge Pineda Lezama
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Intelligence System ◽  
Data Context

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

scholarly journals Atualização da doutrina de gerenciamento de crises: Incidentes policiais e centros de consciência situacional C5I na quarta revolução industrial

2020 ◽  
Vol 13 (1) ◽  
pp. 49-59
Author(s):  
Paulo Augusto Aguilar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence

O presente artigo tem como objetivo abordar a atualização de gerenciamento de crises. Trata-se de um tema bastante importante para a atividade policial, pois visa a expandir a possibilidade das polícias brasileiras, seja militar, civil ou federal, de investigar delitos diversos, relacionados à segurança pública, ao fornecer conceitos de Big Data, Data Mining, Data Storytelling e Business Intelligence como forma de gerar melhor consciência situacional e imagem operacional comum de incidentes de todos os tipos e tamanhos, tudo isso com a flexibilidade de aplicativos disponíveis em smartphones, em tempo real, agilizando a capacidade de resposta e de adaptação do Estado diante de cenários VUCA, utilizado para descrever cenários caracterizados por volatilidade (volatility), incerteza (uncertainty), complexidade (complexity) e ambiguidade (ambiguity).

scholarly journals Open Innovation and Social Big Data for Sustainability: Evidence from the Tourism Industry

2018 ◽  
Vol 10 (9) ◽  
pp. 3215 ◽  
Author(s):  
Pasquale Del Vecchio ◽  
Gioconda Mele ◽  
Valentina Ndou ◽  
Giustina Secundo
Keyword(s):  
Big Data ◽  
Open Innovation ◽  
Tourism Industry ◽  
Longitudinal Case Study ◽  
Huge Amount ◽  
Knowledge Assets ◽  
Social Big Data ◽  
Tourism Experience ◽  
Innovation Processes

This paper aims to contribute to the debate on Open Innovation in the age of Big Data by shedding new light on the role that social networks can play as enabling platforms for tourists’ involvement and sources for the creation and management of valuable knowledge assets. The huge amount of data generated on social media by tourists related to their travel experiences can be a valid source of open innovation. To achieve this aim, this paper presents evidence of a digital tourism experience, through a longitudinal case study of a destination in Apulia, a Southern European region. The findings of the study demonstrate how social Big Data could open up innovation processes that could be of support in defining sustainable tourism experiences in a destination.

THE RELATIONSHIP OF SOCIOTECHNICAL AND TECHNOLOGICAL ASPECTS OF THE INTRODUCTION AND APPLICATION OF BUSINESS INTELLIGENCE SYSTEMS

2021 ◽  
Vol 5 (12) ◽  
pp. 30-35
Author(s):  
Edward N. Ozhiganov ◽  
◽  
Alexander A. Chursin ◽  
Alexey D. Linkov ◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
New Technologies ◽  
Business Analytics ◽  
Scientific Publications ◽  
Business Decisions ◽  
Business Decision ◽  
Business Intelligence Systems ◽  
Demand Fluctuations ◽  
New Research

This article describes a relation between sociotechnical and technological factors involved in launching and implementing Business Intelligence systems. Advanced BI systems include business analytics, data mining, data visualization, data tools and infrastructure, and advanced IT solutions to support business decisions based on big data. Various industries and businesses handle large amounts of data to adapt to changing markets and demand fluctuations, push new technologies, and repair ineffective strategies, etc. With an upsurge in data sizes, more and more new research papers are published today to describe BI implemen-tation, use and results. However, today most studies and scientific publications focus on Business Intelligence technological challenges, while sociotechnical aspects – that is processes involved in business decision mak-ing based on big data – are studied in much rarer cases.

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

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